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QuixiAI

AGI MCP Server

by QuixiAI

consolidate_working_memory

Combine multiple working memory entries into a single semantic memory to organize and preserve information for AI systems.

Instructions

Consolidate multiple working memories into a single semantic memory

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
working_memory_idsYesArray of working memory UUIDs to consolidate
consolidated_contentYesContent for the consolidated memory
consolidated_embeddingYesEmbedding for the consolidated memory

Implementation Reference

  • The core handler function that performs the consolidation: creates a new semantic memory, establishes consolidation relationships, marks originals as consolidated, and logs the event in a database transaction.
    async consolidateWorkingMemory(workingMemoryIds, consolidatedContent, consolidatedEmbedding) {
      try {
        const result = await this.db.transaction(async (tx) => {
          // Create consolidated memory
          const [consolidatedMemory] = await tx
            .insert(schema.memories)
            .values({
              type: 'semantic',
              content: consolidatedContent,
              embedding: consolidatedEmbedding,
              importance: 0.8,
              status: 'active'
            })
            .returning();
    
          const consolidatedId = consolidatedMemory.id;
    
          // Create relationships from working memories to consolidated memory
          for (const workingId of workingMemoryIds) {
            await tx.insert(schema.memoryRelationships).values({
              fromMemoryId: workingId,
              toMemoryId: consolidatedId,
              relationshipType: 'consolidation',
              strength: 1.0
            });
    
            // Mark working memory as consolidated
            await tx
              .update(schema.memories)
              .set({ status: 'consolidated' })
              .where(eq(schema.memories.id, workingId));
          }
    
          // Record consolidation event
          await tx.insert(schema.memoryChanges).values({
            memoryId: consolidatedId,
            changeType: 'consolidation',
            newValue: { source_memories: workingMemoryIds }
          });
    
          return consolidatedMemory;
        });
    
        return result;
      } catch (error) {
        console.warn('Memory consolidation failed:', error.message);
        throw error;
      }
    }
  • Tool schema definition including input validation structure for the consolidate_working_memory tool.
    name: "consolidate_working_memory",
    description: "Consolidate multiple working memories into a single semantic memory",
    inputSchema: {
      type: "object",
      properties: {
        working_memory_ids: {
          type: "array",
          items: { type: "string" },
          description: "Array of working memory UUIDs to consolidate"
        },
        consolidated_content: {
          type: "string",
          description: "Content for the consolidated memory"
        },
        consolidated_embedding: {
          type: "array",
          items: { type: "number" },
          description: "Embedding for the consolidated memory"
        }
      },
      required: ["working_memory_ids", "consolidated_content", "consolidated_embedding"]
    }
  • mcp.js:626-632 (registration)
    MCP server tool call handler registration: switch case that dispatches the tool call to the memoryManager's consolidateWorkingMemory method and formats the response.
    case "consolidate_working_memory":
      const consolidatedMemory = await memoryManager.consolidateWorkingMemory(
        args.working_memory_ids,
        args.consolidated_content,
        args.consolidated_embedding
      );
      return { content: [{ type: "text", text: JSON.stringify(consolidatedMemory, null, 2) }] };
  • Tool schema provided in MCP server's listTools response for client validation.
    name: "consolidate_working_memory",
    description: "Consolidate multiple working memories into a single semantic memory",
    inputSchema: {
      type: "object",
      properties: {
        working_memory_ids: {
          type: "array",
          items: { type: "string" },
          description: "Array of working memory UUIDs to consolidate"
        },
        consolidated_content: {
          type: "string",
          description: "Content for the consolidated memory"
        },
        consolidated_embedding: {
          type: "array",
          items: { type: "number" },
          description: "Embedding for the consolidated memory"
        }
      },
      required: ["working_memory_ids", "consolidated_content", "consolidated_embedding"]
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states the tool consolidates memories but doesn't disclose critical behavioral traits: whether this is a destructive operation (what happens to the original working memories), what permissions or authentication might be required, rate limits, or what the output looks like (since no output schema exists).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded with the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a memory consolidation operation with no annotations and no output schema, the description is insufficient. It doesn't explain what happens to the original working memories, what the resulting semantic memory contains, or any prerequisites or side effects, leaving significant gaps for an AI agent to understand the tool's behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters with clear descriptions. The description adds no additional meaning about parameters beyond what's in the schema, such as format expectations for UUIDs, content guidelines, or embedding specifications.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('consolidate') and resources ('multiple working memories into a single semantic memory'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'create_memory' or 'prune_memories', which might also involve memory transformation or management.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'create_memory', 'prune_memories', and 'archive_old_memories' available, there's no indication of when consolidation is appropriate versus creation, deletion, or archiving of memories.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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